Iterative Learning and Repetitive Controller Design Via Duality with Experimental Verification

A duality has been shown to exist between iterative learning and repetitive control, in which both control paradigms differ only in the location of an internal model of the disturbance. In this paper the framework is used to derive novel controllers, which are realised using state and output feedback for the ILC and RC cases respectively. These can be configured in both current error feedback or past error feedforward structures, and complete a quarterion of state/output feedback designs for ILC/RC. The proposed controllers are shown to increase the set of plants for which the tracking task is achievable. A solution to output feedback design problem is formulated, and experimental results using a gantry robot confirm high levels of performance